Reconstructing pictures with machine learning [demonstration]¶
In this post I demonstrate how different techniques of machine learning are working.
The idea used is very simple:
- each black & white image can be treated as a function of 2 variables - x1 and x2, position of pixel
- intensity of pixel is output
- this 2 dimnetional function is very complex
- we can leave only small fraction of pixels, treating others as 'lost'
- by looking how different reression algorithms reconstruct picture, we can get some understanding of how those are operating
Don't treat this demonstration as some 'comparison of approaches', because this problem (reconstructing a picture) is very specific and has very few in common with typical ML datasets. And of course, this approach is not to be used in practice to reconstruc pictures :)
I am using scikit-learn and making use of its API, enabling user to construct new models via meta-ensembling and pipelines.
# !pip install image from PIL import Image %pylab inline
Populating the interactive namespace from numpy and matplotlib
import numpy from sklearn.pipeline import make_pipeline from sklearn.ensemble import RandomForestRegressor, BaggingRegressor, GradientBoostingRegressor, AdaBoostRegressor from sklearn.cross_validation import train_test_split from sklearn.random_projection import GaussianRandomProjection from sklearn.tree import DecisionTreeRegressor from sklearn.linear_model import LinearRegression from sklearn.kernel_approximation import RBFSampler from sklearn.preprocessing import StandardScaler from sklearn.svm import SVR from sklearn.neighbors import KNeighborsRegressor from rep.metaml import FoldingRegressor from rep.estimators import XGBoostRegressor, TheanetsRegressor
!wget http://static.boredpanda.com/blog/wp-content/uploads/2014/08/cat-looking-at-you-black-and-white-photography-1.jpg -O image.jpg # !wget http://orig05.deviantart.net/1d93/f/2009/084/5/2/new_york_black_and_white_by_morgadu.jpg -O image.jpg
--2016-02-16 15:40:20-- http://static.boredpanda.com/blog/wp-content/uploads/2014/08/cat-looking-at-you-black-and-white-photography-1.jpg Resolving static.boredpanda.com... 126.96.36.199 Connecting to static.boredpanda.com|188.8.131.52|:80... connected. HTTP request sent, awaiting response... 200 OK Length: 80728 (79K) [image/jpeg] Saving to: 'image.jpg' image.jpg 100%[=====================>] 78.84K --.-KB/s in 0.05s 2016-02-16 15:40:20 (1.41 MB/s) - 'image.jpg' saved [80728/80728]
image = numpy.asarray(Image.open('./image.jpg')).mean(axis=2) plt.figure(figsize=[20, 10]) plt.imshow(image, cmap='gray')
<matplotlib.image.AxesImage at 0x1184b6150>
Define a function to train regressor¶
train_size is how many pixels shall be used in reconstructing the picture. By default, the algorithm will use only 2% of pixels
def train_display(regressor, image, train_size=0.02): height, width = image.shape flat_image = image.reshape(-1) xs = numpy.arange(len(flat_image)) % width ys = numpy.arange(len(flat_image)) // width data = numpy.array([xs, ys]).T target = flat_image trainX, testX, trainY, testY = train_test_split(data, target, train_size=train_size, random_state=42) mean = trainY.mean() regressor.fit(trainX, trainY - mean) new_flat_picture = regressor.predict(data) + mean plt.figure(figsize=[20, 10]) plt.subplot(121) plt.imshow(image, cmap='gray') plt.subplot(122) plt.imshow(new_flat_picture.reshape(height, width), cmap='gray')